import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()


def 	lowerCamelCase					(	UpperCamelCase     :				str			,       UpperCamelCase     :				str			,       UpperCamelCase     :				str							)   ->    Dict:
		def get_masked_lm_array(UpperCamelCase     :				str							):
				_lowerCamelCase				   =  F"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
				_lowerCamelCase				   =  tf.train.load_variable(UpperCamelCase			,       UpperCamelCase							)

				if "kernel" in name:
						_lowerCamelCase				   =  array.transpose()

				return torch.from_numpy(UpperCamelCase							)

		def get_encoder_array(UpperCamelCase     :				str							):
				_lowerCamelCase				   =  F"""encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
				_lowerCamelCase				   =  tf.train.load_variable(UpperCamelCase			,       UpperCamelCase							)

				if "kernel" in name:
						_lowerCamelCase				   =  array.transpose()

				return torch.from_numpy(UpperCamelCase							)

		def get_encoder_layer_array(UpperCamelCase     :				int			,       UpperCamelCase     :				str							):
				_lowerCamelCase				   =  F"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
				_lowerCamelCase				   =  tf.train.load_variable(UpperCamelCase			,       UpperCamelCase							)

				if "kernel" in name:
						_lowerCamelCase				   =  array.transpose()

				return torch.from_numpy(UpperCamelCase							)

		def get_encoder_attention_layer_array(UpperCamelCase     :				int			,       UpperCamelCase     :				str			,       UpperCamelCase     :				str							):
				_lowerCamelCase				   =  F"""encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
				_lowerCamelCase				   =  tf.train.load_variable(UpperCamelCase			,       UpperCamelCase							)
				_lowerCamelCase				   =  array.reshape(UpperCamelCase							)

				if "kernel" in name:
						_lowerCamelCase				   =  array.transpose()

				return torch.from_numpy(UpperCamelCase							)

		print(F"""Loading model based on config from {config_path}..."""							)
		_lowerCamelCase				   =  BertConfig.from_json_file(UpperCamelCase							)
		_lowerCamelCase				   =  BertForMaskedLM(UpperCamelCase							)

		# Layers
		for layer_index in range(0			,       config.num_hidden_layers							):
				_lowerCamelCase				   =  model.bert.encoder.layer[layer_index]

				# Self-attention
				_lowerCamelCase				   =  layer.attention.self

				_lowerCamelCase				   =  get_encoder_attention_layer_array(
				    UpperCamelCase			,       '_query_dense/kernel'			,       self_attn.query.weight.data.shape							)
				_lowerCamelCase				   =  get_encoder_attention_layer_array(
				    UpperCamelCase			,       '_query_dense/bias'			,       self_attn.query.bias.data.shape							)
				_lowerCamelCase				   =  get_encoder_attention_layer_array(
				    UpperCamelCase			,       '_key_dense/kernel'			,       self_attn.key.weight.data.shape							)
				_lowerCamelCase				   =  get_encoder_attention_layer_array(
				    UpperCamelCase			,       '_key_dense/bias'			,       self_attn.key.bias.data.shape							)
				_lowerCamelCase				   =  get_encoder_attention_layer_array(
				    UpperCamelCase			,       '_value_dense/kernel'			,       self_attn.value.weight.data.shape							)
				_lowerCamelCase				   =  get_encoder_attention_layer_array(
				    UpperCamelCase			,       '_value_dense/bias'			,       self_attn.value.bias.data.shape							)

				# Self-attention Output
				_lowerCamelCase				   =  layer.attention.output

				_lowerCamelCase				   =  get_encoder_attention_layer_array(
				    UpperCamelCase			,       '_output_dense/kernel'			,       self_output.dense.weight.data.shape							)
				_lowerCamelCase				   =  get_encoder_attention_layer_array(
				    UpperCamelCase			,       '_output_dense/bias'			,       self_output.dense.bias.data.shape							)

				_lowerCamelCase				   =  get_encoder_layer_array(UpperCamelCase			,       '_attention_layer_norm/gamma'							)
				_lowerCamelCase				   =  get_encoder_layer_array(UpperCamelCase			,       '_attention_layer_norm/beta'							)

				# Intermediate
				_lowerCamelCase				   =  layer.intermediate

				_lowerCamelCase				   =  get_encoder_layer_array(UpperCamelCase			,       '_intermediate_dense/kernel'							)
				_lowerCamelCase				   =  get_encoder_layer_array(UpperCamelCase			,       '_intermediate_dense/bias'							)

				# Output
				_lowerCamelCase				   =  layer.output

				_lowerCamelCase				   =  get_encoder_layer_array(UpperCamelCase			,       '_output_dense/kernel'							)
				_lowerCamelCase				   =  get_encoder_layer_array(UpperCamelCase			,       '_output_dense/bias'							)

				_lowerCamelCase				   =  get_encoder_layer_array(UpperCamelCase			,       '_output_layer_norm/gamma'							)
				_lowerCamelCase				   =  get_encoder_layer_array(UpperCamelCase			,       '_output_layer_norm/beta'							)

		# Embeddings
		_lowerCamelCase				   =  get_encoder_array('_position_embedding_layer/embeddings'							)
		_lowerCamelCase				   =  get_encoder_array('_type_embedding_layer/embeddings'							)
		_lowerCamelCase				   =  get_encoder_array('_embedding_norm_layer/gamma'							)
		_lowerCamelCase				   =  get_encoder_array('_embedding_norm_layer/beta'							)

		# LM Head
		_lowerCamelCase				   =  model.cls.predictions.transform

		_lowerCamelCase				   =  get_masked_lm_array('dense/kernel'							)
		_lowerCamelCase				   =  get_masked_lm_array('dense/bias'							)

		_lowerCamelCase				   =  get_masked_lm_array('layer_norm/gamma'							)
		_lowerCamelCase				   =  get_masked_lm_array('layer_norm/beta'							)

		_lowerCamelCase				   =  get_masked_lm_array('embedding_table'							)

		# Pooling
		_lowerCamelCase				   =  BertPooler(config=UpperCamelCase							)
		_lowerCamelCase				   =  get_encoder_array('_pooler_layer/kernel'							)
		_lowerCamelCase				   =  get_encoder_array('_pooler_layer/bias'							)

		# Export final model
		model.save_pretrained(UpperCamelCase							)

		# Integration test - should load without any errors ;)
		_lowerCamelCase				   =  BertForMaskedLM.from_pretrained(UpperCamelCase							)
		print(new_model.eval()							)

		print('Model conversion was done sucessfully!'							)


if __name__ == "__main__":
				A						=			argparse.ArgumentParser()
				parser.add_argument(
				    '--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow Token Dropping checkpoint path.'
				)
				parser.add_argument(
				    '--bert_config_file',
				    type=str,
				    required=True,
				    help='The config json file corresponding to the BERT model. This specifies the model architecture.',
				)
				parser.add_argument(
				    '--pytorch_dump_path',
				    type=str,
				    required=True,
				    help='Path to the output PyTorch model.',
				)
				A						=			parser.parse_args()
				convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
from abc import ABC, abstractmethod
from argparse import ArgumentParser





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		@staticmethod
		@abstractmethod
		def   _snake_case       (       snake_case__     :     ArgumentParser		)      ->	Tuple:
				raise NotImplementedError()



		@abstractmethod
		def   _snake_case       (       self     :     Optional[Any]		)      ->	Optional[Any]:
				raise NotImplementedError()